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K-Nearest Neighbors (KNN)

This repository provides an implementation of the K-Nearest Neighbors (KNN) algorithm in Python. KNN is a simple, yet powerful, machine learning algorithm used for both classification and regression tasks. The repository includes the algorithm's implementation along with various examples and applications using different datasets.

Overview

The K-Nearest Neighbors (KNN) algorithm is a non-parametric, instance-based learning algorithm that makes predictions based on the closest training examples in the feature space. Given a new data point, KNN finds the K nearest neighbors and assigns the most common label (for classification) or average value (for regression) to the new point.

Key Concepts:

  • Classification: The most common class among the nearest neighbors is assigned to the data point.
  • Regression: The average of the target values of the nearest neighbors is used for prediction.
  • Distance Metrics: Commonly used distance metrics include Euclidean distance, Manhattan distance, and others.
  • Value of K: The choice of K (number of neighbors) plays a critical role in the model's performance.

Libraries and Tools Used

  • Scikit-learn: For implementing the KNN algorithm and evaluation metrics.
  • Pandas: For data manipulation and preparation.
  • NumPy: For numerical operations.
  • Matplotlib/Seaborn: For data visualization and result plotting.